A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV

TL;DR

This paper introduces a deep neural network-based beamforming method for dual-function THz UAV systems that jointly optimize communication and sensing performance with reduced computation time.

Contribution

It proposes a dual DNN framework for real-time beamforming and GBS association in UAV-based THz systems, improving efficiency and accuracy over existing methods.

Findings

Enhanced beamforming speed compared to traditional optimizers

Improved SINR performance in simulations

Effective joint optimization of communication and sensing

Abstract

In this paper, we consider a scenario with one UAV equipped with a ULA, which sends combined information and sensing signals to communicate with multiple GBS and, at the same time, senses potential targets placed within an interested area on the ground. We aim to jointly design the transmit beamforming with the GBS association to optimize communication performance while ensuring high sensing accuracy. We propose a predictive beamforming framework based on a dual DNN solution to solve the formulated nonconvex optimization problem. A first DNN is trained to produce the required beamforming matrix for any point of the UAV flying area in a reduced time compared to state-of-the-art beamforming optimizers. A second DNN is trained to learn the optimal mapping from the input features, power, and EIRP constraints to the GBS association decision. Finally, we provide an extensive simulation analysis to corroborate the proposed approach and show the benefits of EIRP, SINR performance and computational speed.